Overview

Dataset statistics

Number of variables13
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory50.9 KiB
Average record size in memory52.1 B

Variable types

Categorical1
Numeric12

Alerts

pattern_angle is uniformly distributedUniform
step_strip has unique valuesUnique
density_strip has unique valuesUnique
ratio_filler_matrix has unique valuesUnique
density has unique valuesUnique
elasticity_module has unique valuesUnique
number_hardeners has unique valuesUnique
content_epoxy_groups has unique valuesUnique
surface_density has unique valuesUnique
elasticity_module_stretching has unique valuesUnique
strapery_strength has unique valuesUnique

Reproduction

Analysis started2023-04-09 15:12:35.608321
Analysis finished2023-04-09 15:12:53.352179
Duration17.74 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

pattern_angle
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
500 
90
500 

Length

Max length2
Median length1.5
Mean length1.5
Min length1

Characters and Unicode

Total characters1500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 500
50.0%
90 500
50.0%

Length

2023-04-09T18:12:53.426959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T18:12:53.508876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 500
50.0%
90 500
50.0%

Most occurring characters

ValueCountFrequency (%)
0 1000
66.7%
9 500
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1000
66.7%
9 500
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1000
66.7%
9 500
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1000
66.7%
9 500
33.3%

step_strip
Real number (ℝ)

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9118404
Minimum0.037638936
Maximum14.440522
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-04-09T18:12:53.602615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.037638936
5-th percentile2.7883562
Q15.1391077
median6.9134443
Q38.5667441
95-th percentile11.376188
Maximum14.440522
Range14.402883
Interquartile range (IQR)3.4276364

Descriptive statistics

Standard deviation2.5578339
Coefficient of variation (CV)0.37006553
Kurtosis0.0062151742
Mean6.9118404
Median Absolute Deviation (MAD)1.6999707
Skewness0.10782123
Sum6911.8404
Variance6.5425138
MonotonicityNot monotonic
2023-04-09T18:12:53.706073image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.856166363 1
 
0.1%
7.73243618 1
 
0.1%
10.85324192 1
 
0.1%
7.518721104 1
 
0.1%
8.101343155 1
 
0.1%
9.127882004 1
 
0.1%
4.395253658 1
 
0.1%
4.063182831 1
 
0.1%
4.685389996 1
 
0.1%
6.632902145 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
0.03763893619 1
0.1%
0.1450341493 1
0.1%
0.2396603227 1
0.1%
0.2687555552 1
0.1%
0.3057225943 1
0.1%
0.3108153343 1
0.1%
0.3901906908 1
0.1%
0.5718348622 1
0.1%
0.7302598953 1
0.1%
0.9394624829 1
0.1%
ValueCountFrequency (%)
14.44052219 1
0.1%
14.37645149 1
0.1%
14.05138302 1
0.1%
14.03321552 1
0.1%
13.73240471 1
0.1%
13.65398693 1
0.1%
13.57192039 1
0.1%
13.4849453 1
0.1%
13.37877178 1
0.1%
13.37485504 1
0.1%

density_strip
Real number (ℝ)

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.245335
Minimum11.740126
Maximum103.9889
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-04-09T18:12:53.831057image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum11.740126
5-th percentile36.037541
Q149.970739
median57.413593
Q365.107235
95-th percentile77.435953
Maximum103.9889
Range92.248774
Interquartile range (IQR)15.136496

Descriptive statistics

Standard deviation12.33887
Coefficient of variation (CV)0.21554368
Kurtosis0.52134246
Mean57.245335
Median Absolute Deviation (MAD)7.5919647
Skewness-0.14570996
Sum57245.335
Variance152.24771
MonotonicityNot monotonic
2023-04-09T18:12:53.924795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64.30196381 1
 
0.1%
57.87047195 1
 
0.1%
54.19389725 1
 
0.1%
72.81317902 1
 
0.1%
65.49714661 1
 
0.1%
54.49599457 1
 
0.1%
47.04286575 1
 
0.1%
61.76455307 1
 
0.1%
58.60848999 1
 
0.1%
55.35163498 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
11.74012566 1
0.1%
15.41607571 1
0.1%
17.93440819 1
0.1%
19.25053406 1
0.1%
20.57163239 1
0.1%
23.14340019 1
0.1%
23.89819527 1
0.1%
24.28352356 1
0.1%
24.86051178 1
0.1%
25.68285561 1
0.1%
ValueCountFrequency (%)
103.9888992 1
0.1%
98.2026062 1
0.1%
92.96349335 1
0.1%
92.04213715 1
0.1%
89.87661743 1
0.1%
88.80764771 1
0.1%
88.07248688 1
0.1%
86.01242828 1
0.1%
85.98717499 1
0.1%
85.66099548 1
0.1%

ratio_filler_matrix
Real number (ℝ)

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9306116
Minimum0.3894026
Maximum5.5917416
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-04-09T18:12:54.034155image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.3894026
5-th percentile1.4929071
Q12.3185266
median2.9078323
Q33.5525389
95-th percentile4.4677314
Maximum5.5917416
Range5.202339
Interquartile range (IQR)1.2340124

Descriptive statistics

Standard deviation0.91393942
Coefficient of variation (CV)0.31185962
Kurtosis-0.19070825
Mean2.9306116
Median Absolute Deviation (MAD)0.6284759
Skewness0.078648709
Sum2930.6116
Variance0.83528531
MonotonicityNot monotonic
2023-04-09T18:12:54.143522image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.587347746 1
 
0.1%
3.905552626 1
 
0.1%
3.888005018 1
 
0.1%
2.419589281 1
 
0.1%
1.085399032 1
 
0.1%
4.594119072 1
 
0.1%
1.934229136 1
 
0.1%
3.476583481 1
 
0.1%
4.545325756 1
 
0.1%
3.085230112 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
0.3894025981 1
0.1%
0.4633420408 1
0.1%
0.5473909974 1
0.1%
0.5515084863 1
0.1%
0.5967829227 1
0.1%
0.7492232323 1
0.1%
0.7904994488 1
0.1%
0.827015698 1
0.1%
0.8573807478 1
0.1%
0.8754576445 1
0.1%
ValueCountFrequency (%)
5.591741562 1
0.1%
5.455566406 1
0.1%
5.452959538 1
0.1%
5.425139427 1
0.1%
5.314143658 1
0.1%
5.295842171 1
0.1%
5.258894444 1
0.1%
5.210442543 1
0.1%
5.120368481 1
0.1%
5.110780716 1
0.1%

density
Real number (ℝ)

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1975.6668
Minimum1731.7646
Maximum2207.7734
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-04-09T18:12:54.253144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1731.7646
5-th percentile1852.5322
Q11924.2034
median1977.5743
Q32021.1595
95-th percentile2099.6479
Maximum2207.7734
Range476.00879
Interquartile range (IQR)96.956055

Descriptive statistics

Standard deviation73.796814
Coefficient of variation (CV)0.037352865
Kurtosis0.063801207
Mean1975.6668
Median Absolute Deviation (MAD)48.254395
Skewness0.048927709
Sum1975666.8
Variance5445.9697
MonotonicityNot monotonic
2023-04-09T18:12:54.365174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1953.274902 1
 
0.1%
1911.819458 1
 
0.1%
2150.337646 1
 
0.1%
1983.725342 1
 
0.1%
1845.739136 1
 
0.1%
1935.85144 1
 
0.1%
1849.078369 1
 
0.1%
1948.515869 1
 
0.1%
1820.140137 1
 
0.1%
2023.658569 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
1731.764648 1
0.1%
1740.657471 1
0.1%
1784.4823 1
0.1%
1786.035645 1
0.1%
1797.648193 1
0.1%
1801.940674 1
0.1%
1804.84021 1
0.1%
1805.735962 1
0.1%
1807.595947 1
0.1%
1810.357788 1
0.1%
ValueCountFrequency (%)
2207.773438 1
0.1%
2192.73877 1
0.1%
2192.297607 1
0.1%
2184.493164 1
0.1%
2182.751709 1
0.1%
2172.246826 1
0.1%
2170.342285 1
0.1%
2161.565186 1
0.1%
2160.751465 1
0.1%
2158.794922 1
0.1%

elasticity_module
Real number (ℝ)

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean739.95052
Minimum2.4369087
Maximum1911.5365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-04-09T18:12:54.474771image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2.4369087
5-th percentile180.75979
Q1498.43807
median741.1481
Q3962.85141
95-th percentile1271.4531
Maximum1911.5365
Range1909.0996
Interquartile range (IQR)464.41334

Descriptive statistics

Standard deviation330.327
Coefficient of variation (CV)0.44641768
Kurtosis-0.23323014
Mean739.95052
Median Absolute Deviation (MAD)231.08607
Skewness0.10196706
Sum739950.52
Variance109115.93
MonotonicityNot monotonic
2023-04-09T18:12:54.584136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1136.596191 1
 
0.1%
665.9319458 1
 
0.1%
856.6282349 1
 
0.1%
102.7016754 1
 
0.1%
676.6216431 1
 
0.1%
626.9717407 1
 
0.1%
957.3956299 1
 
0.1%
692.9330444 1
 
0.1%
1099.171387 1
 
0.1%
609.4979858 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
2.436908722 1
0.1%
4.339154243 1
0.1%
9.986209869 1
0.1%
11.3129425 1
0.1%
21.77199364 1
0.1%
23.61460304 1
0.1%
26.82699966 1
0.1%
27.67505646 1
0.1%
31.53459167 1
0.1%
31.60846901 1
0.1%
ValueCountFrequency (%)
1911.536499 1
0.1%
1815.865112 1
0.1%
1649.415649 1
0.1%
1615.096924 1
0.1%
1588.677246 1
0.1%
1572.096069 1
0.1%
1546.290894 1
0.1%
1543.023682 1
0.1%
1542.168457 1
0.1%
1511.681885 1
0.1%

number_hardeners
Real number (ℝ)

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.54112
Minimum17.740274
Maximum198.9532
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-04-09T18:12:54.706055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum17.740274
5-th percentile61.070482
Q192.170591
median110.16267
Q3130.31197
95-th percentile155.55423
Maximum198.9532
Range181.21293
Interquartile range (IQR)38.141378

Descriptive statistics

Standard deviation28.30447
Coefficient of variation (CV)0.25605377
Kurtosis0.10385396
Mean110.54112
Median Absolute Deviation (MAD)19.070633
Skewness-0.045436364
Sum110541.12
Variance801.14301
MonotonicityNot monotonic
2023-04-09T18:12:54.815419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
137.6274261 1
 
0.1%
122.8963547 1
 
0.1%
154.9200287 1
 
0.1%
113.8807373 1
 
0.1%
95.37519836 1
 
0.1%
125.8435822 1
 
0.1%
90.56858063 1
 
0.1%
116.3962097 1
 
0.1%
109.7037659 1
 
0.1%
115.1743393 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
17.74027443 1
0.1%
29.95615005 1
0.1%
32.01922226 1
0.1%
33.62418747 1
0.1%
35.59969711 1
0.1%
35.62090302 1
0.1%
38.66849899 1
0.1%
40.30480576 1
0.1%
41.42913818 1
0.1%
41.88627625 1
0.1%
ValueCountFrequency (%)
198.9532013 1
0.1%
192.8516998 1
0.1%
192.7053833 1
0.1%
192.3344727 1
0.1%
191.053009 1
0.1%
190.3181 1
0.1%
188.0498962 1
0.1%
181.8284454 1
0.1%
181.0328064 1
0.1%
179.6459656 1
0.1%

content_epoxy_groups
Real number (ℝ)

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.24385
Minimum14.254986
Maximum28.955095
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-04-09T18:12:54.924781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum14.254986
5-th percentile18.308985
Q120.558296
median22.230762
Q323.982114
95-th percentile26.176658
Maximum28.955095
Range14.700109
Interquartile range (IQR)3.4238181

Descriptive statistics

Standard deviation2.4069993
Coefficient of variation (CV)0.10820966
Kurtosis-0.29254279
Mean22.24385
Median Absolute Deviation (MAD)1.7256956
Skewness-0.032753933
Sum22243.85
Variance5.7936454
MonotonicityNot monotonic
2023-04-09T18:12:55.034148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.34453392 1
 
0.1%
23.16970062 1
 
0.1%
23.27620697 1
 
0.1%
19.84963417 1
 
0.1%
19.31984329 1
 
0.1%
20.91900253 1
 
0.1%
22.9047966 1
 
0.1%
26.96686935 1
 
0.1%
26.1723671 1
 
0.1%
16.99385071 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
14.25498581 1
0.1%
15.69589424 1
0.1%
15.88166714 1
0.1%
16.04897881 1
0.1%
16.39159393 1
0.1%
16.66753197 1
0.1%
16.70410728 1
0.1%
16.71543694 1
0.1%
16.77981949 1
0.1%
16.84853554 1
0.1%
ValueCountFrequency (%)
28.95509529 1
0.1%
28.9074707 1
0.1%
28.84890175 1
0.1%
28.62011528 1
0.1%
28.32496834 1
0.1%
27.92084312 1
0.1%
27.81318283 1
0.1%
27.70768166 1
0.1%
27.63374329 1
0.1%
27.52134132 1
0.1%

flash_temperature
Real number (ℝ)

Distinct999
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean285.91129
Minimum160.25584
Maximum413.27341
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-04-09T18:12:55.143519image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum160.25584
5-th percentile220.4047
Q1258.53919
median285.85396
Q3313.58144
95-th percentile352.48336
Maximum413.27341
Range253.01756
Interquartile range (IQR)55.042252

Descriptive statistics

Standard deviation40.962757
Coefficient of variation (CV)0.14327086
Kurtosis-0.17173873
Mean285.91129
Median Absolute Deviation (MAD)27.451523
Skewness0.033345431
Sum285911.29
Variance1677.9474
MonotonicityNot monotonic
2023-04-09T18:12:55.268560image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300.3028259 2
 
0.2%
234.7168884 1
 
0.1%
267.3693848 1
 
0.1%
215.7370148 1
 
0.1%
313.184967 1
 
0.1%
274.2256775 1
 
0.1%
262.8352356 1
 
0.1%
290.1737366 1
 
0.1%
290.2169189 1
 
0.1%
257.679657 1
 
0.1%
Other values (989) 989
98.9%
ValueCountFrequency (%)
160.2558441 1
0.1%
173.4849243 1
0.1%
173.9739075 1
0.1%
179.3743896 1
0.1%
186.508606 1
0.1%
187.5623474 1
0.1%
187.9779663 1
0.1%
188.9186707 1
0.1%
189.2083893 1
0.1%
189.8670807 1
0.1%
ValueCountFrequency (%)
413.273407 1
0.1%
403.6528625 1
0.1%
397.1512756 1
0.1%
396.8982239 1
0.1%
386.0679932 1
0.1%
385.8947754 1
0.1%
385.6977844 1
0.1%
383.9384155 1
0.1%
382.7597961 1
0.1%
381.8577271 1
0.1%

surface_density
Real number (ℝ)

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean483.02466
Minimum0.60373992
Maximum1399.5424
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-04-09T18:12:55.378002image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.60373992
5-th percentile62.293187
Q1268.05746
median452.97226
Q3694.21036
95-th percentile969.28507
Maximum1399.5424
Range1398.9386
Interquartile range (IQR)426.15289

Descriptive statistics

Standard deviation280.81174
Coefficient of variation (CV)0.58136107
Kurtosis-0.43321681
Mean483.02466
Median Absolute Deviation (MAD)211.08783
Skewness0.37890035
Sum483024.66
Variance78855.234
MonotonicityNot monotonic
2023-04-09T18:12:55.487417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
555.8934326 1
 
0.1%
284.9047852 1
 
0.1%
401.7593384 1
 
0.1%
422.6278687 1
 
0.1%
628.3687744 1
 
0.1%
505.8564758 1
 
0.1%
238.4981079 1
 
0.1%
249.9842529 1
 
0.1%
488.873291 1
 
0.1%
75.56736755 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
0.6037399173 1
0.1%
1.668002248 1
0.1%
1.89409256 1
0.1%
4.099043369 1
0.1%
6.77925539 1
0.1%
6.986692905 1
0.1%
7.09535408 1
0.1%
8.460317612 1
0.1%
9.04620266 1
0.1%
11.29522419 1
0.1%
ValueCountFrequency (%)
1399.542358 1
0.1%
1391.032349 1
0.1%
1291.340088 1
0.1%
1288.691895 1
0.1%
1238.47644 1
0.1%
1230.46521 1
0.1%
1227.243042 1
0.1%
1182.326172 1
0.1%
1181.341431 1
0.1%
1177.612671 1
0.1%
Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.328462
Minimum64.054062
Maximum82.682053
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-04-09T18:12:55.596776image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum64.054062
5-th percentile68.360186
Q171.301756
median73.247593
Q375.379738
95-th percentile78.891516
Maximum82.682053
Range18.627991
Interquartile range (IQR)4.0779819

Descriptive statistics

Standard deviation3.1195838
Coefficient of variation (CV)0.042542606
Kurtosis-0.13526498
Mean73.328462
Median Absolute Deviation (MAD)2.0623817
Skewness0.12375973
Sum73328.462
Variance9.7318029
MonotonicityNot monotonic
2023-04-09T18:12:55.715153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80.80322266 1
 
0.1%
75.25041199 1
 
0.1%
70.42533875 1
 
0.1%
74.21709442 1
 
0.1%
78.89582825 1
 
0.1%
74.02639771 1
 
0.1%
77.18180847 1
 
0.1%
72.193573 1
 
0.1%
75.13460541 1
 
0.1%
69.04740143 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
64.05406189 1
0.1%
64.6964035 1
0.1%
65.5533371 1
0.1%
65.79384613 1
0.1%
65.9799881 1
0.1%
66.05274963 1
0.1%
66.22123718 1
0.1%
66.26528931 1
0.1%
66.42079163 1
0.1%
66.42106628 1
0.1%
ValueCountFrequency (%)
82.68205261 1
0.1%
82.52577209 1
0.1%
82.23760223 1
0.1%
81.59474945 1
0.1%
81.41712952 1
0.1%
81.20314789 1
0.1%
81.17523193 1
0.1%
81.05329132 1
0.1%
80.97096252 1
0.1%
80.80322266 1
0.1%

strapery_strength
Real number (ℝ)

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2467.1843
Minimum1036.8566
Maximum3848.4368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-04-09T18:12:55.839707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1036.8566
5-th percentile1673.5873
Q12143.8347
median2461.2493
Q32760.163
95-th percentile3308.61
Maximum3848.4368
Range2811.5802
Interquartile range (IQR)616.32831

Descriptive statistics

Standard deviation485.6246
Coefficient of variation (CV)0.19683353
Kurtosis0.060260721
Mean2467.1843
Median Absolute Deviation (MAD)310.84753
Skewness0.081669316
Sum2467184.3
Variance235831.27
MonotonicityNot monotonic
2023-04-09T18:12:55.949113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2587.343018 1
 
0.1%
2121.639404 1
 
0.1%
2181.809082 1
 
0.1%
1706.640137 1
 
0.1%
3409.525635 1
 
0.1%
3130.33374 1
 
0.1%
2435.075439 1
 
0.1%
2066.567383 1
 
0.1%
2431.940918 1
 
0.1%
2039.022217 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
1036.856567 1
0.1%
1071.123779 1
0.1%
1143.210327 1
0.1%
1145.065796 1
0.1%
1188.307373 1
0.1%
1250.392822 1
0.1%
1264.310669 1
0.1%
1269.728149 1
0.1%
1325.301758 1
0.1%
1337.424683 1
0.1%
ValueCountFrequency (%)
3848.436768 1
0.1%
3817.269531 1
0.1%
3791.072754 1
0.1%
3773.151855 1
0.1%
3763.445068 1
0.1%
3725.190674 1
0.1%
3705.672607 1
0.1%
3694.298096 1
0.1%
3693.676514 1
0.1%
3689.223633 1
0.1%

resin_consumption
Real number (ℝ)

Distinct999
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean218.38688
Minimum33.803024
Maximum414.59064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-04-09T18:12:56.358601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum33.803024
5-th percentile121.71361
Q1179.19049
median217.27701
Q3257.49566
95-th percentile313.9987
Maximum414.59064
Range380.78761
Interquartile range (IQR)78.305168

Descriptive statistics

Standard deviation59.819778
Coefficient of variation (CV)0.27391654
Kurtosis-0.12098043
Mean218.38688
Median Absolute Deviation (MAD)39.359497
Skewness0.013386143
Sum218386.88
Variance3578.406
MonotonicityNot monotonic
2023-04-09T18:12:56.490020image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
224.0982056 2
 
0.2%
110.8015137 1
 
0.1%
112.8941879 1
 
0.1%
175.7679138 1
 
0.1%
326.7035828 1
 
0.1%
259.9171143 1
 
0.1%
127.1822357 1
 
0.1%
189.2994232 1
 
0.1%
246.2671204 1
 
0.1%
147.8318787 1
 
0.1%
Other values (989) 989
98.9%
ValueCountFrequency (%)
33.80302429 1
0.1%
41.04827881 1
0.1%
53.54891586 1
0.1%
63.68569946 1
0.1%
64.52417755 1
0.1%
72.53087616 1
0.1%
74.09786224 1
0.1%
75.83181 1
0.1%
76.02307129 1
0.1%
82.5831604 1
0.1%
ValueCountFrequency (%)
414.5906372 1
0.1%
402.1638184 1
0.1%
386.9034424 1
0.1%
383.6633911 1
0.1%
378.7568665 1
0.1%
359.0522156 1
0.1%
356.9259644 1
0.1%
355.7586975 1
0.1%
354.9580994 1
0.1%
350.6721191 1
0.1%

Interactions

2023-04-09T18:12:51.608267image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:35.880589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:37.240447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:38.583252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:40.191010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:41.524434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:43.036940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:44.424457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:45.790401image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:47.182948image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:48.841395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:50.289755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:51.724112image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:35.989352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:37.356219image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:38.675871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:40.307730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:41.643396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:43.168631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:44.541013image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:45.916825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:47.288278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:48.943364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:50.407720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:51.859095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:36.107425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:37.459375image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:38.797641image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:40.408632image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:41.740955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:43.305908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:44.657806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:46.023199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:47.390231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:49.057379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:50.508746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:51.965590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:36.221512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:37.574351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:38.891019image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:40.525848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:41.875795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:43.399318image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:44.757637image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:46.141745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:47.507199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:49.195926image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:50.622553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:52.073482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:36.325365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:37.674902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:38.989953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:40.617378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:42.001611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:43.516283image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:44.859912image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:46.263804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:47.863863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:49.306327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:50.722681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:52.207303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:36.424379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:37.789406image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:39.430975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:40.738290image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:42.106565image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:43.623407image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:44.972658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:46.388468image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:47.993328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:49.439484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:50.823987image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:52.326369image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:36.541044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:37.908164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:39.533246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:40.842043image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:42.245189image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:43.764017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:45.090557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:46.500881image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:48.117202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:49.556110image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:50.950381image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:52.455919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:36.659298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:38.030751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:39.642143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:40.956643image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:42.397010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:43.858797image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:45.212074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:46.606692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:48.239207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:49.675845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:51.072825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:52.573344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:36.768143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:38.160012image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:39.757720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:41.057667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:42.523294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:43.974458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:45.308254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:46.719315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:48.356124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:49.774709image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:51.174982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:52.690511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:36.875779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:38.266901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:39.856610image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:41.184609image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:42.642129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:44.088682image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:45.440320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:46.842407image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:48.468507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:49.890577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:51.290510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:52.806705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:36.989263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:38.356617image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:39.982188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:41.295409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:42.792735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:44.208914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:45.555614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:46.964120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:48.574306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:50.040592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:51.412849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:52.920486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:37.107532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:38.460592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:40.096546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:41.411339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:42.906892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:44.307803image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:45.677039image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:47.064339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:48.708648image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:50.157351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-09T18:12:51.516393image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-04-09T18:12:56.583755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
step_stripdensity_stripratio_filler_matrixdensityelasticity_modulenumber_hardenerscontent_epoxy_groupsflash_temperaturesurface_densityelasticity_module_stretchingstrapery_strengthresin_consumptionpattern_angle
step_strip1.0000.006-0.004-0.0570.018-0.033-0.0110.003-0.0180.026-0.0610.0030.143
density_strip0.0061.0000.0510.022-0.015-0.002-0.030-0.0100.0160.019-0.013-0.0050.378
ratio_filler_matrix-0.0040.0511.0000.0030.033-0.0070.023-0.019-0.011-0.0180.0310.0590.000
density-0.0570.0220.0031.000-0.017-0.028-0.015-0.0300.053-0.032-0.077-0.0370.000
elasticity_module0.018-0.0150.033-0.0171.0000.0350.0020.032-0.011-0.0000.040-0.0000.000
number_hardeners-0.033-0.002-0.007-0.0280.0351.0000.0070.0800.046-0.074-0.060-0.0070.000
content_epoxy_groups-0.011-0.0300.023-0.0150.0020.0071.000-0.000-0.0080.067-0.0250.0140.000
flash_temperature0.003-0.010-0.019-0.0300.0320.080-0.0001.0000.0230.022-0.0250.0530.000
surface_density-0.0180.016-0.0110.053-0.0110.046-0.0080.0231.0000.0140.011-0.0180.010
elasticity_module_stretching0.0260.019-0.018-0.032-0.000-0.0740.0670.0220.0141.0000.0160.0480.063
strapery_strength-0.061-0.0130.031-0.0770.040-0.060-0.025-0.0250.0110.0161.0000.0200.000
resin_consumption0.003-0.0050.059-0.037-0.000-0.0070.0140.053-0.0180.0480.0201.0000.000
pattern_angle0.1430.3780.0000.0000.0000.0000.0000.0000.0100.0630.0000.0001.000

Missing values

2023-04-09T18:12:53.087049image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-09T18:12:53.265600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

pattern_anglestep_stripdensity_stripratio_filler_matrixdensityelasticity_modulenumber_hardenerscontent_epoxy_groupsflash_temperaturesurface_densityelasticity_module_stretchingstrapery_strengthresin_consumption
007.85616664.3019642.5873481953.2749021136.596191137.62742622.344534234.716888555.89343380.8032232587.343018246.613113
107.40154319.2505342.4999181942.595825901.519958146.25221323.081757351.231873864.72546476.1780783705.672607226.222763
206.67578078.6232992.0464712037.631836707.570862101.61724923.146393312.307220547.60119673.8170702624.026367178.198563
307.52639838.1769751.8564762018.220337836.294373135.40170326.435514327.510376150.96144177.2107622473.187256123.344559
408.32569946.0454293.3055351917.907471478.286255105.78692617.874100328.154572526.69213972.3457113059.032959275.575867
507.65621133.5710222.7095541892.071167641.05255196.56329322.989290262.956726804.59259074.5113602288.967285126.816338
6010.30294539.2342802.2828252008.357544393.967316149.37283321.661751330.498627535.37146072.2449262704.445068261.077057
708.94689172.0845951.9781401973.629150991.724121149.37213119.750578232.058197485.45376675.6657032448.943115162.493698
803.74662557.9977721.7714361872.491577801.03387579.79454822.296303340.736908864.92919970.9475942796.785400123.356262
909.09436344.8016013.2770872010.046997339.55041567.49899324.280609254.949081117.53523367.4787062462.605469207.018585
pattern_anglestep_stripdensity_stripratio_filler_matrixdensityelasticity_modulenumber_hardenerscontent_epoxy_groupsflash_temperaturesurface_densityelasticity_module_stretchingstrapery_strengthresin_consumption
990903.48951258.8280372.3103941931.146851554.01031596.74977922.146486214.82772856.24276078.1436081939.30749587.270142
991908.21203252.0738071.6462352014.772583841.064819102.97990421.073366271.490845615.16815279.1544722518.516113232.428207
992907.20150361.0970042.8065631872.864624996.018677146.19918821.559290313.900482799.63409472.8155522443.482910307.265167
993908.43015868.3557973.7458621914.629395680.683716110.97910325.922634309.796387628.36456376.0305562466.925537152.184723
994906.71136454.0594412.7587282000.506104934.564392143.02186621.379519273.85269265.10596567.6337513102.539551229.780365
995908.08811147.7591782.2713461952.087891912.85553086.99218020.123249324.774567209.19870073.0909582387.292480125.007668
996907.61913866.9319313.4440222050.089111444.732635145.98197919.599770254.215408350.66082872.9208302360.392822117.730095
997909.80092572.8582843.2806041972.372925416.836517110.53347823.957502248.423050740.14276174.7343442662.906006236.606766
9989010.07985965.5194783.7053512066.799805741.475525141.39796419.246944275.779846641.46814074.0427092071.715820197.126068
999909.02104366.9201433.8080201890.413452417.316223129.18341127.474764300.952698758.74786474.3097082856.328857194.754349